Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal
Author | Chowdhury, Moajjem H. |
Author | Shuzan, Md N. |
Author | Chowdhury, Muhammad E. H. |
Author | Reaz, Mamun B. |
Author | Mahmud, Sakib |
Author | Al Emadi, Nasser |
Author | Ayari, Mohamed A. |
Author | Ali, Sawal H. |
Author | Bakar, Ahmad Ashrif A. |
Author | Rahman, Syed M. |
Author | Khandakar, Amith |
Available date | 2023-04-17T06:57:42Z |
Publication Date | 2022 |
Publication Name | Bioengineering |
Resource | Scopus |
Abstract | Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients. 2022 by the authors. |
Sponsor | This work was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, a member of Qatar Foundation, Doha, Qatar and Grant numbers DIP-2020-004 and GUP-2021-019 from Universiti Kebangsaan Malaysia. The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | MDPI |
Subject | ConvMixer convolutional neural networks deep learning machine learning photoplethysmogram respiration rate |
Type | Article |
Issue Number | 10 |
Volume Number | 9 |
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